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dc.contributor.advisorAndreou, Andreas G.en_US
dc.contributor.authorMurray, Thomas Simmonsen_US
dc.date.accessioned2015-09-16T03:35:48Z
dc.date.available2015-09-16T03:35:48Z
dc.date.created2015-05en_US
dc.date.issued2015-02-20en_US
dc.date.submittedMay 2015en_US
dc.identifier.urihttp://jhir.library.jhu.edu/handle/1774.2/37891
dc.description.abstractThis dissertation explores computational methods to address the problem of physics-based modeling and ultimately doing inference from data in multiple modalities where there exists large amounts of low dimensional data complementary to a much smaller set of high dimensional data. In this instance the low dimensional timeseries data are active acoustics from a micro-Doppler sensor that include no or very limited spatial information, and the high dimensional data is RGB-Depth skeleton data from a Microsoft Kinect sensor. The task is that of human action recognition from the active acoustic data. To accomplish this, statistical models, trained simultaneously on both the micro-Doppler modulations induced by human actions and symbolic representations of skeletal poses, are developed. This enables the model to learn correlations between the rich temporal structure of the micro-Doppler modulations and the high-dimensional motion sequences of human action. During runtime, the model then relies purely on the active acoustic data to infer the human action. In order to adapt this methodology to situations not observed in the training data, a physical model of the human body is combined with a physics-based simulation of the Doppler phenomenon to predict the acoustic data for a sequence of skeletal poses and a con gurable sensor geometry. The physics model is then combined with a generative statistical model for human actions to create a generative physics-based model of micro-Doppler modulations for human action.en_US
dc.format.mimetypeapplication/pdfen_US
dc.languageen
dc.publisherJohns Hopkins University
dc.subjectultrasounden_US
dc.subjectactive acousticsen_US
dc.subjecthuman action recognitionen_US
dc.subjectaction recognitionen_US
dc.subjectmachine learningen_US
dc.subjectdeep belief networken_US
dc.subjecthidden Markov modelen_US
dc.titleHuman Action Recognition from Active Acoustics: Physics Modelling for Representation Learning and Inference Using Generative Probabilistic Graphical Modelsen_US
dc.typeThesisen_US
thesis.degree.disciplineElectrical Engineeringen_US
thesis.degree.grantorJohns Hopkins Universityen_US
thesis.degree.grantorWhiting School of Engineeringen_US
thesis.degree.levelDoctoralen_US
thesis.degree.namePh.D.en_US
dc.type.materialtexten_US
thesis.degree.departmentElectrical and Computer Engineeringen_US
dc.contributor.committeeMemberEtienne-Cummings, Ralphen_US
dc.contributor.committeeMemberElhilali, Mounyaen_US
dc.contributor.committeeMemberPouliquen, Philippe O.en_US
dc.contributor.committeeMemberRizk, Charbel G.en_US


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